Event occurence place estimation method, computer-readable recording medium storing event occurrence place estimation program, and event occurrence place estimation apparatus

ABSTRACT

An event-occurrence-place estimation method having a process executed by a computer, the process includes acquiring a message of a predetermined event from social media to which a message is posted; extracting occurrence place information indicating an occurrence place of the predetermined event from the acquired messages; ranking the occurrence places in descending order according to the number of acquired messages corresponding to each of the pieces of occurrence place information, cumulating, for each of the occurrence places, the number of acquired messages of the occurrence place and the number of acquired messages of the occurrence places which are ranked higher than the occurrence place information, and identifying a change point where an increase rate of the cumulated number of the messages is an average increase ratio; and outputting the piece of occurrence place information identified by the identified change point.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority of theprior Japanese Patent Application No. 2014-086724, filed on Apr. 18,2014, the entire contents of which are incorporated herein by reference.

FIELD

The embodiments discussed herein are related to an event occurrenceplace estimation method, a computer-readable recording medium storingevent occurrence place estimation program, and an event occurrence placeestimation apparatus.

BACKGROUND

At scenes of disaster management, when a disaster occurs, it isimportant to instantaneously catch a disaster occurrence place. However,it is difficult to set a physical sensor that catches a disaster eventin every single place where there is a risk of occurrence of a disaster.Thus, it is examined to use disaster witness reports through socialmedia as “sensors by human”. The social media in this connection ismedia used by users for positing and exchanging messages on line andthereby performing information distribution. Examples of social mediainclude Twitter (a registered trademark of Twitter, Inc.), Facebook (aregistered trademark of Facebook, Inc.), and the like. For example,there is a technique in which a message related to a disaster isextracted from social media to extract information related to a disasteroccurrence place.

Related art is described in, for example, Japanese Laid-open PatentPublication No. 2014-6735, Japanese Laid-open Patent Publication No.2013-50919, and Japanese Laid-open Patent Publication No. 2013-235527.

SUMMARY

According to an aspect of the invention, an event occurrence placeestimation method having a process executed by a computer, the processincludes acquiring a message related to a predetermined event fromsocial media to which a message is posted; extracting a piece ofoccurrence place information indicating an occurrence place of thepredetermined event from the acquired messages; ranking the occurrenceplaces in descending order according to the number of acquired messagescorresponding to each of the pieces of occurrence place information,cumulating, for each of the occurrence places, the number of acquiredmessages associated with the occurrence place and the number of acquiredmessages associated with the occurrence places which are ranked higherthan the occurrence place, and identifying a change point where anincrease rate of the cumulated number of the messages is an averageincrease ratio; and outputting the piece of occurrence place informationidentified by the identified change point.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating an example of a schematic configurationof an entire system including an event occurrence place estimationapparatus;

FIG. 2 is a diagram illustrating an example of a functionalconfiguration of an event occurrence place estimation apparatus;

FIG. 3 is a diagram illustrating an example of messages posted to asocial media service;

FIG. 4 is a graph illustrating an example of a cumulative compositionratio curve;

FIG. 5 is a diagram illustrating a map on which occurrence placesincluded in messages when a disaster occurs are indicated;

FIG. 6 is a graph illustrating a flow of identifying a change point froma cumulative composition ratio curve;

FIG. 7 is a graph illustrating an example of cumulative compositionratio curves obtained when the number of correct answers differs;

FIG. 8 is a graph illustrating an example of cumulative compositionratio curves obtained when a noise amount differs;

FIG. 9A is a graph illustrating a comparison example;

FIG. 9B is a graph illustrating a comparison example;

FIG. 9C is a graph illustrating a comparison example;

FIG. 10A is a table illustrating an example of results of rankingflooding and submerging occurrence places, which were extracted frommessages, in descending order of the number of messages;

FIG. 10B is a graph illustrating the number of messages of eachoccurrence place and an example of a cumulative composition ratio curve;

FIG. 10C is a table illustrating an example of evaluation results foraccuracy of estimation of flooding and submerging occurrence places;

FIG. 11A is a table illustrating an example of results of rankingflooding and submerging occurrence places, which were extracted frommessages, in descending order of the number of messages;

FIG. 11B is a graph illustrating the number of messages of eachoccurrence place and an example of a cumulative composition ratio curve;

FIG. 11C is a table illustrating an example of evaluation results foraccuracy of estimation of flooding and submerging occurrence places;

FIG. 12 is a flow chart illustrating an example of steps of outputprocessing; and

FIG. 13 is a diagram illustrating a computer that executes an eventoccurrence place estimation program.

DESCRIPTION OF EMBODIMENTS

When the above-described technique in the background is used, disasteroccurrence place candidates may be extracted. However, disasteroccurrence place candidates that are extracted from social media includea correct answer and a wrong answer, and a disaster occurrence place isnot accurately estimated.

The number of disaster occurrence places differs depending on the sizeof a disaster occurrence range. Thus, for example, a case is assumedwhere disaster occurrence place candidates are ranked in descendingorder of the number of messages and candidates of a predetermined numberof the top ranks are extracted as disaster occurrence places. In thiscase, there are cases where all of disaster occurrence places are notextracted. Also, there are cases where a place where a disaster has notactually occurred is extracted. For example, a case is assumed where thenumber of messages are tallied for disaster occurrence place candidatesin ranking order and then a cumulative composition ratio relative to thenumber of all messages is obtained to extract disaster occurrence placesfor which the cumulative composition ratio is up to a predeterminedvalue. Also, in this case, there are cases where all of disasteroccurrence places are not extracted. There are also cases where a placewhere a disaster has not actually occurred is extracted.

Note that the above-described problems are not limited to estimation ofdisaster occurrence places, and may occur generally when an eventoccurrence place where an event has occurred is estimated from socialmedia.

In one aspect, it is desired to provide an event occurrence placeestimation method, a computer-readable recording medium storing eventoccurrence place estimation program, and an event occurrence placeestimation apparatus, which enable highly accurate estimation of anevent occurrence place.

Hereinafter, embodiments of an event occurrence place estimation method,an event occurrence place estimation program, and an event occurrenceplace estimation apparatus according to the present disclosure will bedescribed in detail with reference to the accompanying drawings. Notethat the present disclosure is not limited by the following embodiments.Each of embodiments may be combined, as appropriate, to the extent thatthere is no contradiction.

First Embodiment

[Configuration of System]

A system according to a first embodiment will be described. FIG. 1 is adiagram illustrating an example of a schematic configuration of anentire system including an event occurrence place estimation apparatus.As illustrated in FIG. 1, a system 10 includes an event occurrence placeestimation apparatus 11 and a social media service 12. The eventoccurrence place estimation apparatus 11 and the social media service 12are coupled to each other via a network 13 so as to be able tocommunicate with each other, and are enabled to exchange various typesof information. As an example of the network 13, whether wired orwireless, a mobile communication, such as a mobile phone, and the like,or a communication network of any kind, such as the Internet, a localarea network (LAN), a virtual private network (VPN), and the like, maybe employed.

The social media service 12 is a cloud system that provides a socialmedia service in which users post and exchange messages to performinformation distribution. The social media service 12 may be implementedby a single computer, or may be implemented by a plurality of computers.Examples of the social media service 12 include Twitter, Facebook, andthe like.

The event occurrence place estimation apparatus 11 is an apparatus thatestimates an occurrence place where a predetermined event has occurredfrom messages posted to the social media service 12. The eventoccurrence place estimation apparatus 11 is, for example, a computer,such as a personal computer, a server computer, and the like. The eventoccurrence place estimation apparatus 11 may be implemented as a singlecomputer, and may be implemented as a cloud realized by a plurality ofcomputers. Note that, in this embodiment, an example where the eventoccurrence place estimation apparatus 11 is a single computer will bedescribed. Also, in this embodiment, an example where the predeterminedevent is a disaster and an occurrence place where the disaster hasoccurred is estimated will be described.

[Configuration of Event Occurrence Place Estimation Apparatus]

Next, a configuration of the event occurrence place estimation apparatus11 according to this embodiment will be described. FIG. 2 is a diagramillustrating an example of a functional configuration of an eventoccurrence place estimation apparatus. As illustrated in FIG. 2, theevent occurrence place estimation apparatus 11 includes a communicationinterface (I/F) section 20, a display section 21, an input section 22, astorage section 23, and a control section 24. The event occurrence placeestimation apparatus 11 may include, in addition to the functionalsections illustrated in FIG. 2, various types of known functionalsections.

The communication I/F section 20 is an interface that performscommunication control with another apparatus. The communication I/Fsection 20 transmits and receives various types of information withanother apparatus via the network 13. For example, the communication I/Fsection 20 receives information related to a posted message from thesocial media service 12. As the communication I/F section 20, a networkinterface card, such as a LAN card, and the like, may be used.

The display section 21 is a display device that displays various typesof information. As the display section 21, a display device, such as aliquid crystal display (LCD), a cathode ray tube (CRT), and the like,may be used. The display section 21 displays various types ofinformation. For example, the display section 21 displays various typesof screens, such as a screen of an estimation result of an occurrenceplace where an event has occurred.

The input section 22 is an input device that inputs various types ofinformation. As the input section 22, an input device that receivesinput of an operation of a mouse, a keyboard, or the like, may be used.The input section 22 receives input of various types of information. Forexample, the input section 22 receives input of various types ofsettings and various types of operations which are related to estimationof an occurrence place where an event has occurred. The input section 22receives operation input from a user, and inputs operation informationindicating received operation contents to the control section 24.

The storage section 23 is a storage device that stores various types ofdata. For example, the storage section 23 is a storage device, such as ahard disk, a solid state drive (SSD), an optical disk, and the like.Note that the storage section 23 may be a semiconductor memory, such asa random access memory (RAM), a flash memory, a non-volatile staticrandom access memory (NVSRAM), and the like, in which data may berewritten.

The storage section 23 stores an operating system (OS) and various typesof programs, which are executed by the control section 24. For example,the storage section 23 stores various types of programs including aprogram that executes estimation processing that will be describedlater. Furthermore, the storage section 23 stores various types of dataused by a program executed by the control section 24. For example, thestorage section 23 stores event information 30, message information 31,and cumulative information 32.

The event information 30 is data that stores information related to anevent that is a target of occurrence place estimation. For example, akeyword related to an event is stored in the event information 30. Inthis embodiment, a message including the keyword stored in the eventinformation 30 is extracted from the social media service 12. Forexample, when an occurrence place of a flood disaster, such as flooding,submerging, and the like, is estimated as an event, “flooding” and“submerging” as keywords related to a flooding disaster are stored inthe event information 30. A keyword that is stored in the eventinformation 30 is set in accordance with an event that is a target ofoccurrence place estimation. Note that the event information 30 may beconfigured such that addition, change, and deletion of a keyword areexternally performed even after setting has been performed.

The message information 31 is data that stores a message related to anevent acquired from the social media service 12. A message that isacquired will be described in detail later.

The cumulative information 32 is data that stores information related toa cumulative total value of the number of messages for each eventoccurrence place. A cumulative total value of the number of messages foreach event occurrence place will be described in detail later.

The control section 24 is a device that controls the event occurrenceplace estimation apparatus 11. As the control section 24, an electroniccircuit, such as a central processing unit (CPU), a micro processingunit (MPU), and the like, or an integrated circuit, such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), and the like, may be used. The control section 24includes an internal memory that stores a program in which processingsteps of various types are defined and control data, and executesvarious types of processing using the program or the control data.Various types of programs are operated, and thus, the control section 24functions as various types of processing sections. For example, thecontrol section 24 includes an acquisition section 35, a filter section36, an extraction section 37, an identification section 38, and anoutput section 39.

The acquisition section 35 performs various types of acquisition. Forexample, the acquisition section 35 acquires a posted message from thesocial media service 12. Note that the social media service 12 maytransmit a message as needed and also may transmit a message whenreceiving a request from the event occurrence place estimation apparatus11. Also, the social media service 12 may transmit all of postedmessages, and may selectively transmit only a message related to aspecific event. For example, the acquisition section 35 may transmit akeyword stored in the event information 30 to the social media service21 and the social media service 12 may selectively transmit only amessage including the keyword to the event occurrence place estimationapparatus 11.

The filter section 36 performs various types of filter processing. Forexample, the filter section 36 performs filter processing of excluding amessage that is not used from messages acquired by the acquisitionsection 35. For example, when the social media service 12 transmits allof posted messages to the apparatus 11, the filter section 36 performsfilter processing of excluding a message that does not include a keywordstored in the event information 30 from acquired messages.

When estimating an event occurrence place, the filter section 36performs filter processing of excluding a message that is to be noise orunnecessary for an event occurrence place estimation. For example, thefilter section 36 performs filter processing of excluding a messageposted by a media organization, a reposted message, and a presumptivemessage from acquired messages. For example, when a message includesinformation of a posting source and the posting source is a mediaorganization, the filter section 36 excludes the message. Also, when amessage is a reposted message, the filter section 36 excludes themessage. For example, in a twitter, a reposted message includes “RT”indicating a retweeted message. When a message is a retweeted message,the filter section 36 excludes the message. When a message is apresumptive message, the filter section 36 excludes the message. Forexample, when a message includes a specific word included in presumptivecontents, the filter section 36 excludes the message. The specific wordmay be set by an administrator. The specific word may be also obtainedby performing machine learning on collected messages of presumptivecontents. The filter section 36 may syntactically analyze a message and,when a message is determined to include a syntax used in presumptivecontents, the filter section 36 may exclude the message. A syntax usedin presumptive contents may be acquired, for example, by performingmachine learning on collected messages of presumptive contents. In thisembodiment, a case where all of a message posted by a mediaorganization, a reposted message, and a presumptive message are excludedfrom acquired messages will be described, but filter processing ofexcluding one or two types of the above-described messages may beperformed. A message that has not been excluded by filter processing isstored in the message information 31.

The extraction section 37 performs various types of extraction. Forexample, the extraction section 37 extracts information of an eventoccurrence place from messages that have not been excluded by the filtersection 36. For example, the extraction section 37 extracts a noun, suchas a place name, and the like, which indicates a place, from a message.For example, place information in which a noun, such as a place name,and the like, which indicates a place, has been registered in advance isstored in the storage section 23 and, when information of a place, suchas a place name, registered in the place information, is included in amessage, the extraction section 37 extracts information of the placefrom the message. Note that, when a monitoring target range in which theoccurrence of an event is monitored is defined, the extraction section37 may extract only a noun indicating a place in the monitoring targetrange. In this case, only a place in the monitoring target range may beregistered in the place information or a flag may be given to a place inthe monitoring target range and the place may be thus registered. Also,a noun that indicates a place that is a monitoring target may beregistered as a monitoring target in the place information in advance,and the extraction section 37 may extract only the noun of themonitoring target. For example, when an occurrence place where an event,such as a flooding of a river, and the like, has occurred is estimated,as nouns of monitoring targets, the name of a river and the names ofsurrounding towns and cities through which the river runs may beregistered as nouns of monitoring targets in advance, and the extractionsection 37 may extract a registered noun of a monitoring target. Notethat the extraction section 37 may extract a noun, such as a place name,and the like, included in a message, which indicates a place, as it is,and may extract information of a place related to a noun included in amessage, which indicates a place. For example, the name of a town andthe name of a municipality including the name of the town may beregistered in the place information in association with each other inadvance, and the extraction section 37 may extract the name of amunicipality corresponding to the name of a town included in a message,based on the place information.

The identification section 38 performs various types of identification.For example, the identification section 38 identifies information thatis a threshold when an event occurrence place is extracted from postedcontents of the social media service 12. For example, the identificationsection 38 ranks occurrence places extracted by the extraction section37 in descending order of the number of messages. Then, theidentification section 38 cumulates the number of messages of eachoccurrence place in ranking order, and identifies a change point wherethe increase rate of the cumulated number of messages is an averageincrease rate. For example, the identification section 38 cumulates thenumber of messages of each occurrence place in ranking order, andobtains a cumulative composition ratio curve for all of messages fromwhich event occurrence places have been extracted. The identificationsection 38 identifies a change point where the change rate of thecumulative composition ratio curve is an average increase rate of thecumulative composition ratio curve.

The output section 39 outputs various types of output. For example, theoutput section 39 outputs an occurrence place, based on a change pointidentified by the identification section 38. For example, the outputsection 39 outputs, as an occurrence place where an event has occurred,an occurrence place ranked in higher than the position of the identifiedchange point in a cumulative composition ratio curve. For example, theoutput section 39 outputs each occurrence place to the display section21 in ranking order, and outputs an occurrence place ranked higher thanthe position of the change point to the display section 21 such that theoccurrence place may be identified. Note that, in this embodiment, anoccurrence place ranked higher than the position of the change point isoutput as an occurrence place where an event has occurred, but the lowerlimit for output may be changed using the position of the change pointas a reference. For example, the output section 39 may output, as anoccurrence place where an event has occurred, an occurrence place rankedhigher than the ranking order which is lower or higher than the positionof the change point by a predetermined number. For example, the outputsection 39 may output, as an occurrence place where an event hasoccurred, an occurrence place ranked higher than the order lower thanthe position of the change point by one.

Now, a specific example will be described. FIG. 3 is a diagramillustrating an example of messages posted in a social media service. Inthis case, an example where a place where a flooding disaster, such as aflooding or submerging, and the like, has occurred is estimated fromposted messages will be described.

FIG. 3 illustrates massages posted when a flooding disaster, such as aflooding or submerging, and the like, was occurring. In the example ofFIG. 3, a message saying, “Our house is in Osaka City. Houses 20 metersaway from our house, located at bit lower level than our house, arehaving underfloor flooding.” was posted at Aug. 13, 2012 23:17:22. Also,in the example of FIG. 3, a message saying, “In Neyagawa City, a riveris flooding because of heavy rain and a road is submerged.” was postedat Aug. 14, 2012 00:47:27. Also, in the example of FIG. 3, a messagesaying, “Maybe, an evacuation order was issued in Hirakata, too?” wasposted at Aug. 14, 2012 05:27:18. Also, in the example of FIG. 3, amessage saying, “It looks like, in Osaka City, houses are flooded insome areas. http://xxx.co.jp” was posted at Aug. 14, 2012 05:27:58.Assume that this “http://xxx.co.jp” is an address indicating a postingsource and is the address of a media organization, such as a newspapercompany. Furthermore, in the example of FIG. 3, a message saying,“RT@xxxx: House next door is flooded. Is Uji City OK?” was posted atAug. 14, 2012 05:30:52. This “RT@xxxx” indicates that this message is areposted message, and also indicates that the posting source is xxxx.

When an occurrence place related to a flooding disaster is estimatedfrom posted contents of the social media service 12, the acquisitionsection 35 acquires messages illustrated in FIG. 3 from the social mediaservice 12.

The filter section 36 performs filter processing of excluding a messagethat does not include a keyword stored in the event information 30 fromreceived messages. For example, when an occurrence place related to aflooding disaster is estimated, the filter section 36 performs filteringprocessing of excluding a message that does not include a keyword, suchas a “flooding” or “submerging”. In the example of FIG. 3, each of themessages includes a keyword, such as “flooding” and “submerging”, andtherefore, the messages are not excluded. Thus, noise in estimating aflooding disaster occurrence place may be excluded, and processing loadsmay be reduced.

The filter section 36 performs filter processing of excluding a messageposted by a media organization, a reposted message, and a presumptivemessage from acquired messages. For example, in the example of FIG. 3,“It looks like, in Osaka City, houses are flooded in some areas.http://xxx.co.jp” is a message posted by a media organization, andtherefore, is excluded. Also, “RT@xxxx: House next door is flooded. IsUji City OK?” is a reposted message, and therefore, is excluded. Also,“Maybe, an evacuation order was issued in Hirakata, too?” is a messageincluding a specific presumptive word, that is, “maybe”, and therefore,is excluded.

In this case, a message posted by a media organization is cited by manyusers. Therefore, when ranking of disaster occurrence places isperformed using also messages posted by media organizations, theinfluence of messages posted by media organizations increases, and thus,an occurrence place is not estimated with high accuracy. Therefore, inthis embodiment, a message posted by a media organization is excluded.

A reposted message is also cited by many users by spreading of thereposted message. Therefore, when reposted messages are included,disaster occurrence places of the reposted messages are ranked high,although each user has not actually seen an occurrence place, and anactual disaster occurrence place is not estimated with high accuracy.Therefore, in this embodiment, a reposted message is excluded.

A presumptive message is not posted by a user, who saw a disaster place.Therefore, using a presumptive message, an occurrence place is notestimated with high accuracy. Therefore, in this embodiment, apresumptive message is excluded.

The extraction section 37 extracts a noun indicating a place from theremaining messages that have not been excluded by the filter section 36.For example, in the example of FIG. 3, the extraction section 37extracts “Osaka City” from the message saying, “Our house is in OsakaCity. Houses 20 meters away from our house, located at bit lower levelthan our house, are having underfloor flooding.” Also, the extractionsection 37 extracts “Neyagawa City” from the message saying, “InNeyagawa City, a river is flooding because of heavy rain and a road issubmerged.”

The identification section 38 ranks occurrence places extracted by theextraction section 37 in descending order of the number of messages.Then, the identification section 38 cumulates the number of messages ofeach occurrence place in ranking order and obtains a cumulativecomposition ratio curve for all of messages.

FIG. 4 is a graph illustrating an example of a cumulative compositionratio curve. In the example of FIG. 4, for all of messages, that is, thetotal of messages of the occurrence places of the first to thirteenthranks, the number of messages of each of occurrence places of the firstto thirteenth ranks is indicated by a graph. For example, for the firstrank, the number of messages is 20. A cumulative composition ratio curve40 is a curve obtained by cumulating the composition ratio of eachoccurrence place in raking order.

In this case, a message includes noise that is not excluded by filterprocessing performed by the filter section 36. For example, there arecases where a user posts a massage including a wrong place name bymistake. Also, there are cases where, when the same name place name isused in a plurality of different places, a different place is extractedas a disaster occurrence place. Such a message is noise. That is, thereare messages which include an occurrence place where a disaster actuallyhas occurred, and massages which include a place where a disaster hasnot actually occurred. A place where a disaster has not actuallyoccurred is noise when a disaster occurrence place is estimated.

When disaster occurrence places extracted from messages are ranked indescending order of the number of messages, many messages are posted foran occurrence place where a disaster has actually occurred, and thus,the occurrence place is ranked high. On the other hand, a place where adisaster has not actually occurred is ranked low.

FIG. 5 is a diagram illustrating a map on which occurrence placesincluded in messages when a disaster has occurred are indicated. In theexample of FIG. 5, a disaster occurrence area where a disaster hasoccurred is surrounded by a dashed line. In the example of FIG. 5, whenan occurrence place is in the disaster occurrence area, the occurrenceplace is indicated as a correct answer, and when an occurrence place isoutside the disaster occurrence, the occurrence place is indicated asnoise. When a disaster occurs, many messages related to the disaster areposted in a disaster occurrence area. Therefore, many correct answersconcentrate in the disaster occurrence area. On the other hand,uniformly, noise occurs at random.

In FIG. 4, a part indicating the number of messages of correct answerand a part indicating the number of messages of noise are separatelyillustrated in a graph of the number of messages of each occurrenceplace. Also, in FIG. 4, a cumulative composition ratio curve 41 of onlycorrect answers, obtained by cumulating the composition ratio of thenumber of messages of correct answers for each occurrence place to thetotal number of messages of correct answers. Also, in FIG. 4, acumulative composition ratio curve 42 of only noise, obtained bycumulating the composition ratio of the number of messages of noise foreach occurrence place to the total number of messages of noise isillustrated. As described above, uniformly, noise occurs at random.Therefore, the cumulative composition ratio curve 42 is a linear linethat substantially uniformly increases. On the other hand, thecumulative composition ratio curve 41 indicates a drastic increase atoccurrence places where a disaster has actually occurred. In the exampleof FIG. 4, the occurrence places of the first and second ranks arecorrect answers, and therefore, the cumulative composition ratio curve41 indicates drastic increase at the first and second ranks.

The cumulative composition ratio curve 40 is a combination of thecumulative composition ratio curve 41 of only correct answers and thecumulative composition ratio curve 42 of only noise. Thus, thecumulative composition ratio curve 40 greatly increases at an occurrenceplace of correct answer where a disaster has actually occurred, and theincrease rapidly reduces at the boundary of correct answer and noise.Thus, when a straight line connecting a start point and an end point ofthe cumulative composition ratio curve 40 is used as a reference, thecumulative composition ratio curve 40 reaches a peak between the correctanswer and the wrong answer.

The identification section 38 identifies a change point where a changeratio of a cumulative composition ratio curve is an average increaserate of the cumulative composition ratio curve. FIG. 6 is a graphillustrating a flow of identifying a change point from a cumulativecomposition ratio curve. For example, as illustrated in FIG. 6, theidentification section 38 obtains a straight line 50 connecting astarting point and an end point of the cumulative composition ratiocurve 40. The slope of the straight line 50 is an average increase rateof the cumulative composition ratio curve 40. Then, the identificationsection 38 identifies a change point 51 that is a peak of the cumulativecomposition ratio curve 40 when the straight line 50 is used as areference. For example, the identification section 38 translates thestraight line 50 to identify the change point 51 that is a tangent pointthereof with the cumulative composition ratio curve 40. Note that theidentification section 38 may obtain an inflection point that is a peakby rotating the cumulative composition ratio curve 40 such that thestraight line 50 is an axis in the lateral direction, thereby obtainingthe change point 51.

The output section 39 outputs, as an occurrence place where an event hasoccurred, an occurrence place ranked higher than the position of thechange point identified by the identification section 38. In the exampleof FIG. 6, the output section 39 outputs the occurrence places of thefirst and second ranks, which are ranked higher than the position of thechange point 51.

The number of disaster occurrence places differs depending on the sizeof a disaster occurrence place. However, according to this embodiment, adisaster occurrence place may be estimated with high accuracy byobtaining a change point of a cumulative composition ratio curve. Acumulative composition ratio curve is a combination of correct answerand noise components. Therefore, when the number of disaster occurrenceplaces is large, a cumulative composition ratio curve is moderate andthe position of the inflection point is shifted. FIG. 7 is a graphillustrating an example of cumulative composition ratio curves obtainedwhen the number of correct answers differs. In the example of FIG. 7, acumulative composition ratio curve 60 when the number of occurrenceplaces of correct answers where a disaster has actually occurred is twoand a cumulative composition ratio curve 61 when the number of theoccurrence places of correct answers is four are illustrated. Thecumulative composition ratio curve 61 changes more moderately, ascompared to the cumulative composition ratio curve 60, and a changepoint 63 is shifted from the position of the second rank to the positionof the fourth rank.

In the social media service 12, a noise amount of a message changes.However, according to this embodiment, a disaster occurrence place maybe estimated with high accuracy by obtaining a change point of acumulative composition ratio curve. FIG. 8 is a graph illustrating anexample of cumulative composition ratio curves obtained when a noiseamount differs. In the example of FIG. 8, a cumulative composition ratiocurve 65 when the noise amount is small and a cumulative compositionratio curve 66 when the noise amount is large are illustrated. Each ofthe cumulative composition ratio curves 65 and 66 is a combination ofcorrect and noise components. The cumulative composition ratio curve 65has a smaller noise amount than that of the cumulative composition ratiocurve 66, the ratio of the correct answer component is large, andtherefore, the cumulative composition ratio curve 65 indicates a drasticincrease. However, noise uniformly occurs, and therefore, for thecumulative composition ratio curves 65 and 66, the change point 67substantially does not change.

Now, accuracy of estimation will be described with reference tocomparative examples. Accuracy of estimation of a disaster occurrenceplace when a predetermined number of the top ranking disaster occurrenceplaces are extracted as disaster occurrence places and accuracy ofestimation of a disaster occurrence place when disaster occurrenceplaces for which a cumulative composition ratio is up to a predeterminedvalue are extracted will be described.

FIG. 9A is a graph illustrating a comparison example. In the example ofFIG. 9A, it is assumed that the first and second ranks are correctanswers. In the example of FIG. 9A, when the disaster occurrence placesof the top two ranks are extracted as disaster occurrence places, andwhen the disaster occurrence places for which the cumulative compositionratio is up to 90% (0.90) are extracted, a disaster occurrence place ofa correct answer may be extracted.

However, when the disaster occurrence places of the top two ranks areextracted as disaster occurrence places, the disaster occurrence rangeis large, and thus, when the number of correct answers increases, all ofdisaster occurrence places may not be extracted.

FIG. 9B is a graph illustrating a comparison example. In the example ofFIG. 9B, it is assumed that the first to fifth ranks are correctanswers. In the example of FIG. 9B, when the disaster occurrence placesof the top two ranks are extracted as disaster occurrence places, theoccurrence places of the third to fifth ranks are not extracted. Notethat, in the example of FIG. 9B, when the occurrence places for whichthe cumulative composition ratio is up to 90% (0.90) are extracted, theoccurrence places of the first to fifth ranks, which are correctanswers, may be extracted.

On the other hand, when the disaster occurrence places for which thecumulative composition ratio is up to 90% (0.90) are extracted, with anincreased noise amount, there might be cases where a place where adisaster has not actually occurred is extracted.

FIG. 9C is a graph illustrating a comparison example. In the example ofFIG. 9C, it is assumed that the first and second ranks are correctanswers. In the example of FIG. 9C, when the disaster occurrence placesfor which the cumulative composition ratio is up to 90% (0.90) areextracted, the occurrence places of the first to ninth ranks areextracted. Note that, in the example of FIG. 9C, when the disasteroccurrence places of the top two ranks are extracted as disasteroccurrence places, the occurrence places of the first and second ranks,which are correct answers, may be extracted.

As described above, when disaster occurrence places of a predeterminednumber of the top ranks are extracted as disaster occurrence places, andwhen disaster occurrence places for which the cumulative compositionratio is up to a predetermined value are extracted, there are caseswhere a disaster occurrence place is not estimated with high accuracy.On the other hand, as described with reference to FIGS. 6 to 8, evenwhen the number of occurrence places of correct answers changes or whenthe noise amount is large, the event occurrence place estimationapparatus 11 according to this embodiment may estimate a disasteroccurrence place with high accuracy.

Next, an example of actual estimation of a disaster occurrence placewill be described. First, a result of estimation based on data ofmessages related to flooding and submerging that occurred in OsakaPrefecture on Aug. 18, 2012, which were posted to twitter, will bedescribed. FIG. 10A is a table illustrating an example of results ofranking of flooding and submerging occurrence places, which wereextracted from messages, in descending order of the number of messages.In FIG. 10A, occurrence places where flooding and submerging actuallyoccurred are illustrated as correct answers. The occurrence places whereflooding and submerging actually occurred were identified based on newsreports of newspaper of that day, and the like. The example of FIG. 10Aindicates that flooding and submerging actually occurred in “OsakaCity_Osaka Prefecture” of the first rank and “Takatsuki City_OsakaPrefecture” of the second rank. FIG. 10B is a graph illustrating thenumber of messages of each occurrence place and an example of acumulative composition ratio curve. In FIG. 10B, star signs areillustrated as correct answers on parts of the graph, which indicate theoccurrence places of the first and second ranks where flooding andsubmerging actually occurred. In the example of FIG. 10B, the positionof a change point of a cumulative composition ratio curve is theposition of the second rank. For example, as illustrated in FIG. 10A,the output section 39 outputs each occurrence place to the displaysection 21 in ranking order, and outputs occurrence places ranked higherthan the position of the change point to the display section 21 suchthat the occurrence places ranked higher than the position of the changepoint may be identified by star signs. The event occurrence placeestimation apparatus 11 outputs a result in the above-described manner,and thus, a user who referred to the result is able to correspond anddistinguish each occurrence place included in a message and anoccurrence place where it is presumed that event has occurred from eachother, so that credibility of the output result may be increased. Notethat a display format of displaying an output result, which enablesidentification, is not limited thereto, but may be, for example, anyformat that enables identification of an occurrence place ranked higherthan the position of a change point by, for example, changing a color,changing a pattern, or separating a display position.

FIG. 10C is a table illustrating an example of evaluation results foraccuracy of estimation of flooding and submerging occurrence places. Theaccuracy is the ratio of correct answers to occurrence places output asoccurrence places of flooding and submerging. Accuracy is calculated,for example, based on (the number of correct answers among outputoccurrence places)/(the number of the output occurrence places). Arecall ratio is the ratio at which occurrence places of correct answerswere output. The recall ratio is calculated, for example, based on (thenumber of correct answers among output occurrence places)/(the totalnumber of correct answers). An F value is the ratio indicating how wellcorrect answers have been output among output occurrence places. The Fvalue is calculated, for example, based on 2×accuracy×the recallratio/(accuracy+the recall ratio). The event occurrence place estimationapparatus 11 outputs the disaster occurrence places of the first andsecond, and therefore, as illustrated in FIG. 10C, the accuracy is 1.00,the recall ratio is 1.00, and the F value is 1.00. Also, in FIG. 10C, acase where the disaster occurrence places of up to the fifth rank areextracted as disaster occurrence places and a case where the disasteroccurrence places for which the cumulative composition ratio is up to80% are extracted are illustrated for reference. When the disasteroccurrence places of up to the fifth rank are extracted as disasteroccurrence places, the occurrence places of the third and fifth rankswhere flooding and submerging have not occurred are output as disasteroccurrence places, and therefore, the accuracy is 0.40, the recall ratiois 1.00, and the F value is 0.57. When the disaster occurrence placesfor which the cumulative composition ratio is up to 80% are extracted,the disaster occurrence places of the first and second ranks are outputas disaster occurrence places, and therefore, the accuracy is 1.00, therecall ratio is 1.00, and the F value is 1.00.

Next, a result of estimation based on data of messages related toflooding and submerging that occurred in Osaka Prefecture on Aug. 14,2012, which were posted to Twitter, will be described. FIG. 11A is atable illustrating an example of results of ranking flooding andsubmerging occurrence places, which were extracted from messages, indescending order of the number of messages. Also, in FIG. 11A,occurrence places where flooding and submerging actually occurred areillustrated as correct answers. The occurrence places where flooding andsubmerging actually occurred were identified based on news reports ofnewspaper of that day, and the like. The example of FIG. 11A indicatesthat flooding and submerging actually occurred in “Osaka City_OsakaPrefecture” of the first rank, “Moriguchi City_Osaka Prefecture” of thesecond rank, “Neyagawa City_Osaka Prefecture” of the third rank, and“Takatsuki City_Osaka Prefecture” of the fifth rank. FIG. 11B is a graphillustrating the number of messages of each occurrence place and anexample of a cumulative composition ratio curve. In the example of FIG.11B, star signs are illustrated as correct answers on parts of thegraph, which indicate the occurrence places of the first to third, andfifth ranks where flooding and submerging actually occurred. In theexample of FIG. 11B, the position of a change point of a cumulativecomposition ratio curve is the position of the fifth rank. For example,as illustrated in FIG. 11A, the output section 39 outputs eachoccurrence place to the display section 21 in ranking order, and outputsoccurrence places ranked higher than the position of the change point tothe display section 21 such that the occurrence places ranked higherthan the position of the change point may be identified by star signs.

FIG. 11C is a table illustrating an example of evaluation results foraccuracy of estimation of flooding and submerging occurrence places. Theevent occurrence place estimation apparatus 11 outputs the disasteroccurrence places of the first to fifth ranks as disaster occurrenceplaces, and therefore, as illustrated in FIG. 11C, the accuracy is 0.80,the recall ratio is 1.00, and the F value is 0.88. Also, in FIG. 11C, acase where the disaster occurrence places of up to the fifth rank areextracted as disaster occurrence places and a case where the disasteroccurrence places for which the cumulative composition ratio is up to80% are extracted are illustrated for reference. When the disasteroccurrence places of up to the fifth rank are extracted as disasteroccurrence places, the occurrence places of the first to fifth ranks areoutput as disaster occurrence places, and therefore, the accuracy is0.80, the recall ratio is 1.00, and the F value is 0.88. When thedisaster occurrence places for which the cumulative composition ratio isup to 80% are extracted, the occurrence places of the first to seventhranks are output as disaster occurrence places, and therefore, theaccuracy is 0.57, the recall ratio is 1.00, and the F value is 0.72.

[Flow of Processing]

A flow of estimation processing where the event occurrence placeestimation apparatus 11 according to this embodiment estimates an eventoccurrence place will be described. FIG. 12 is a flow chart illustratingan example of steps of estimation processing. The estimation processingis executed with a predetermined timing, for example, a timing withwhich a start of estimation is instructed by an input section 22. Notethat the estimation processing may be regularly executed to regularlyoutput an estimation result.

As illustrated in FIG. 12, the acquisition section 35 acquires a postedmessage from the social media service 12 (S10). The filter section 36performs filter processing of excluding a message that is not used fromacquired messages (S11). For example, the filter section 36 performsfilter processing of excluding a message that does not include a keywordstored in the event information 30 from the acquired messages. Also, thefilter section 36 performs filter processing of excluding a messageposted by a media organization, a reposted message, and a presumptivemessage from the acquired messages.

The extraction section 37 determines whether or not there is a messagethat has not been excluded by filter processing (S12). When there is notthe message (NO in S12), the process is ended. On the other hand, whenthere is the message (YES in S12), the extraction section 37 extractsinformation of an event occurrence place from the remaining messagesthat have not been excluded by filter processing (S13).

The identification section 38 determines whether or not information ofan event occurrence place was extracted (S14). When information of anevent occurrence place was not extracted (NO in S14), the process isended. On the other hand, when information of an event occurrence placewas extracted (YES in S14), the identification section 38 ranksextracted occurrence places in descending order of the number ofmessages (S15). The identification section 38 cumulates the number ofmessages of each occurrence place in ranking order, and obtains acumulative composition ratio curve for all of messages from which eventoccurrence places were extracted (S16). The identification section 38identifies a change point where the change rate of the cumulativecomposition ratio curve is an average increase rate of the cumulativecomposition ratio curve (S17).

The output section 39 outputs an occurrence place, based on theidentified change point (S18), and the process is ended. For example,the output section 39 outputs each occurrence place to the displaysection 21 in ranking order, and outputs an occurrence place rankedhigher than the position of the change point to the display section 21such that the occurrence place ranked higher than the position of thechange point may be identified.

[Advantages]

As described above, the event occurrence place estimation apparatus 11according to this embodiment acquires a message related to apredetermined event from the social media service 12 to which a messageis posted. The event occurrence place estimation apparatus 11 extractsan event occurrence place from the acquired message. The eventoccurrence place estimation apparatus 11 ranks extracted occurrenceplaces in descending order of the number of messages. The eventoccurrence place estimation apparatus 11 cumulates the number ofmessages of each occurrence place in ranking order, and identifies achange point where the increase rate of the cumulated number of messagesis an average increase rate. For example, the event occurrence placeestimation apparatus 11 cumulates the number of messages of eachoccurrence place in ranking order and obtains a cumulative compositionratio curve for all of messages. The event occurrence place estimationapparatus 11 identifies a change point where the change rate of thecumulative composition ratio curve is an average increase rate of thecumulative composition ratio curve. The event occurrence placeestimation apparatus 11 outputs an occurrence place, based on theidentified change point. Thus, the event occurrence place estimationapparatus 11 may estimate an event occurrence place with high accuracy.

Also, the event occurrence place estimation apparatus 11 according tothis embodiment outputs an occurrence place ranked higher than theposition of the identified change point in a cumulative compositionratio curve. Thus, the event occurrence place estimation apparatus 11may estimate an event occurrence place with high accuracy regardless ofthe number of event occurrence places and the noise amount.

Also, the event occurrence place estimation apparatus 11 according tothis embodiment excludes one or more of a message posted by a mediaorganization, a reposted message, and a presumptive message fromacquired messages. Thus, the event occurrence place estimation apparatus11 may extract a message based on witnessing of each user, andtherefore, may estimate an event occurrence place where many userwitnessed the occurrence of an event with high accuracy.

Also, the event occurrence place estimation apparatus 11 according tothis embodiment outputs each occurrence place in ranking order, andoutputs occurrence places ranked higher than the position of the changepoint such that the occurrence places ranked higher than the position ofthe change point may be identified. Thus, with the event occurrenceplace estimation apparatus 11, a user is able to comprehend anddistinguish each occurrence place included in a message and anoccurrence place where it is presumed that event has occurred from eachother, so that credibility of the output result may be increased.

Second Embodiment

An embodiment related to an apparatus according to the presentdisclosure has been described above, but a technique disclosed hereinmay be implemented by not only the embodiment described above but alsovarious different embodiments. Therefore, another embodiment of thepresent disclosure will be described below.

For example, in the above-described embodiment, a case where, in theevent occurrence place estimation apparatus 11, a result is output tothe display section 21 has been described, but an apparatus according tothe present disclosure is not limited thereto. For example, the eventoccurrence place estimation apparatus 11 may output a result to aterminal device connected thereto via the network 13. Also, the eventoccurrence place estimation apparatus 11 may output a signal that urgesa warning for an estimated occurrence place. Also, the event occurrenceplace estimation apparatus 11 may output an e-mail, or the like, to anadministrator of disaster management of an estimated occurrence place.

Also, in the above-described embodiment, a case where a floodingdisaster occurrence place is estimated as an event occurrence place froma message has been described, but an apparatus according to the presentdisclosure is not limited thereto. For example, an apparatus accordingto the present disclosure may be used, for example, for estimating anoccurrence place of some other disaster. Also, an event is not limitedto a disaster. An event may be any event related to a place. Forexample, a place where cherry blossoms came into bloom, a place wherepollens badly scatter, or the like, may be estimated from a message.

Also, each component element of each unit illustrated in the drawings isfunction conceptual and may not be physically configured as illustratedin the drawings. That is, specific embodiments of disintegration andintegration of each unit are not limited to those illustrated in thedrawings, and all or some of the units may be disintegrated/integratedfunctionally or physically in an arbitrary unit in accordance withvarious loads, use conditions, and the like. For example, respectiveprocessing sections of the acquisition section 35, the filter section36, the extraction section 37, the identification section 38, and theoutput section 39 may be integrated, as appropriate. Also, processing ofeach processing section may be divided to processes of a plurality ofprocessing sections, as appropriate. Furthermore, the whole or a part ofeach processing function performed by each processing section may berealized by a CPU and a program that is analyzed and executed by theCPU, or may be realized as a hardware of a wired logic.

[Event Occurrence Place Estimation Program]

Various types processing described in the above-described embodiment maybe realized by causing a computer system, such as a personal computer, awork station, and the like, to execute a program prepared in advance. Anexample of a computer system that executes a program having the samefunction as that of the above-described embodiment will be described.FIG. 13 is a diagram illustrating a computer that executes an eventoccurrence place estimation program.

As illustrated in FIG. 13, a computer 300 includes a central processingunit (CPU) 310, a hard disk drive (HDD) 320, and a random access memory(RAM) 340. The computer 300, CPU 310, HDD 320, and RAM 340 are connectedto each other via a bus 400.

An event occurrence place estimation program 320 a that exercisessimilar functions to those of the acquisition section 35, the filtersection 36, the extraction section 37, the identification section 38,and the output section 39 is stored in HDD 320 in advance. Note that theevent occurrence place estimation program 320 a may be divided, asappropriate.

HDD 320 stores various types of information. For example, HDD 320 storesOS and various types of data used for production planning.

CPU 310 executes a similar operation to that of each processing sectionof the above-described embodiment by reading out the event occurrenceplace estimation program 320 a from HDD 320 to execute the program 320a. That is, the event occurrence place estimation program 320 a executessimilar operations to those of the acquisition section 35, the filtersection 36, the extraction section 37, the identification section 38,and the output section 39.

Note that there may be cases where the above-described event occurrenceplace estimation program 320 a is not initially stored in HDD 320.

For example, a program is stored in a “portable physical medium”, suchas a flexible disk (FD), a CD-ROM, a DVD disk, a magneto-optical disk,an IC card, and the like, which is inserted to the computer 300, inadvance. The computer 300 may be configured to read out the program fromthe portable physical medium and execute the program.

Furthermore, a program is stored in “another computer (or a server)”, orthe like, connected to the computer 300 via a public line, the Internet,a LAN, a WAN, or the like, in advance. The computer 300 may beconfigured to read out the program from the another computer (or theserver), or the like, and execute the program.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the inventionand the concepts contributed by the inventor to furthering the art, andare to be construed as being without limitation to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although the embodiments of the presentinvention have been described in detail, it should be understood thatthe various changes, substitutions, and alterations could be made heretowithout departing from the spirit and scope of the invention.

What is claimed is:
 1. An event occurrence place estimation methodhaving a process executed by a computer, the process comprising:acquiring a message related to a predetermined event from social mediato which a message is posted; extracting a piece of occurrence placeinformation indicating an occurrence place of the predetermined eventfrom the acquired messages; ranking the occurrence places in descendingorder according to the number of acquired messages corresponding to eachof the pieces of occurrence place information, cumulating, for each ofthe occurrence places, the number of acquired messages associated withthe occurrence place and the number of acquired messages associated withthe occurrence places which are ranked higher than the occurrence place,and identifying a change point where an increase rate of the cumulatednumber of the messages is an average increase ratio; and outputting thepiece of occurrence place information identified by the identifiedchange point.
 2. The event occurrence place estimation method having aprocess executed by a computer according to claim 1, wherein, in theidentifying, the number of acquired messages of each occurrence place iscumulated in ranking order to obtain a cumulative composition ratiocurve for all of the acquired messages, and the change point where achange rate is an average increase rate of the cumulative compositionratio curve is identified using the cumulative composition ratio curve.3. The event occurrence place estimation method having a processexecuted by a computer according to claim 1, wherein, in the outputting,the pieces of occurrence place information ranked higher than the pieceof occurrence place information corresponding to the identified changepoint is output.
 4. The event occurrence place estimation method havinga process executed by a computer according to claim 1, wherein thecomputer further executes excluding one or more of a message posted by amedia organization, a reposted message, and a presumptive message fromthe acquired messages, and in the extracting, the piece of occurrenceplace information indicating the occurrence place is extracted fromremaining messages after the exclusion.
 5. The event occurrence placeestimation method having a process executed by a computer according toclaim 1, wherein, in the outputting, each of the pieces of occurrenceplace information indicating the occurrence place is output in rankingorder, and the piece of occurrence place information indicating theoccurrence place ranked higher than the piece of occurrence informationindicating the occurrence place corresponding to the change point isoutput in a discriminable manner.
 6. A computer-readable recordingmedium storing an event occurrence place estimation program for causinga computer to execute a process, the process comprising: acquiring amessage related to a predetermined event from social media to which amessage is posted; extracting a piece of occurrence place informationindicating an occurrence place of the predetermined event from theacquired messages; ranking the occurrence places in descending orderaccording to the number of acquired messages corresponding to each ofthe pieces of occurrence place information, cumulating, for each of theoccurrence places, the number of acquired messages associated with theoccurrence place and the number of acquired messages associated with theoccurrence places which are ranked higher than the occurrence place, andidentifying a change point where an increase rate of the cumulatednumber of the messages is an average increase ratio; and outputting thepiece of occurrence place information identified by the identifiedchange point.
 7. An event occurrence place estimation apparatuscomprising: an acquiring section that acquires a message related to apredetermined event from social media to which a message is posted; anextraction section that extracts a piece of occurrence place informationindicating an occurrence place of the predetermined event from theacquired messages; an identification section that ranks the occurrenceplaces in descending order according to the number of acquired messagescorresponding to each of the pieces of occurrence place information,cumulates, for each of the occurrence places, the number of acquiredmessages associated with the occurrence place and the number of acquiredmessages associated with the occurrence places which are ranked higherthan the occurrence place, and identifies a change point where anincrease rate of the cumulated number of the messages is an averageincrease ratio; and an outputting section that outputs the pieces ofoccurrence place information identified by the identified change point.